Forecasting the number of Covid-19 cases is a crucial tool in public health policy. In this paper, we construct seasonal autoregressive moving average and autoregressive conditional heteroscedasticity models to forecast the spread of the infection in the UAE. While most of the existing literature is dedicated to forecasting the total number of infections, we endeavor to forecast the number of new infections which is a significantly more challenging task due to the greater volatility. Our models are based on a careful analysis of correlation plots and residual analysis. In addition, we employ highly accurate population data that leads to more reliable outcomes. The results reveal a high degree of accuracy of the proposed forecasting methods. The constructed models can be used by health officials to better anticipate and plan for new cases of Covid-19.
Keywords: AR-ARCH; Autoregression; Covid-19; Forecasting; SARMA.
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